Hi, If I understand your problem correctly, there is a similar JIRA issueFLINK-10348, reported by me. Maybe you can take a look at it.
Jiayi Liao,Best Original Message Sender:Gerard garciager...@talaia.io Recipient:fearsome.lucidityfearsome.lucid...@gmail.com Cc:useru...@flink.apache.org Date:Monday, Oct 29, 2018 17:50 Subject:Re: Unbalanced Kafka consumer consumption The stream is partitioned by key after ingestion at the finest granularity that we can (which is finer than how stream is partitioned when produced to kafka). It is not perfectly balanced but still is not so unbalanced to show this behavior (more balanced than what the lag images show). Anyway, let's assume that the problem is that the stream is so unbalanced that one operator subtask can't handle the ingestion rate. It is expected then that all the others operators reduce its ingestion rate even if they have resources to spare? The task is configured with processing time and there are no windows. If that is the case, is there a way to let operator subtasks process freely even if one of them is causing back pressure upstream? The attached images shows how Kafka lag increases while thethroughput is stable until some operator subtasks finish. Thanks, Gerard On Fri, Oct 26, 2018 at 8:09 PM Elias Levy fearsome.lucid...@gmail.com wrote: You can always shuffle the stream generated by the Kafka source (dataStream.shuffle())to evenly distribute records downstream. On Fri, Oct 26, 2018 at 2:08 AM gerardg ger...@talaia.io wrote: Hi, We are experience issues scaling our Flink application and we have observed that it may be because Kafka messages consumption is not balanced across partitions. The attached image (lag per partition) shows how only one partition consumes messages (the blue one in the back) and it wasn't until it finished that the other ones started to consume at a good rate (actually the total throughput multiplied by 4 when these started) . Also, when that ones started to consume, one partition just stopped an accumulated messages back again until they finished. We don't see any resource (CPU, network, disk..) struggling in our cluster so we are not sure what could be causing this behavior. I can only assume that somehow Flink or the Kafka consumer is artificially slowing down the other partitions. Maybe due to how back pressure is handled? http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/file/t1007/consumer_max_lag.png Gerard -- Sent from: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/
